{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2dc21c28",
   "metadata": {},
   "source": [
    "Exce表格“pydata02”收集了某城市的天气数据，基于“pydata02xlsx”数据集回答以下问题，数据导入后命名为pydata02。以下每道题均是在原始数据基础上进行的，题干所涉及到的数据修改操作不继承到其他题中。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "075bba1f",
   "metadata": {},
   "source": [
    "#### 101、求出pydata02.xlsx列名为'大气温度'的中位数 (A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17ba161e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1c24b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>大气温度</th>\n",
       "      <th>气象站气压</th>\n",
       "      <th>相对湿度</th>\n",
       "      <th>类别</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-28 23:00:00</td>\n",
       "      <td>13.9</td>\n",
       "      <td>719.6</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-28 20:00:00</td>\n",
       "      <td>13.4</td>\n",
       "      <td>716.0</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-28 17:00:00</td>\n",
       "      <td>16.6</td>\n",
       "      <td>713.7</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-28 14:00:00</td>\n",
       "      <td>16.9</td>\n",
       "      <td>714.1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-02-28 11:00:00</td>\n",
       "      <td>12.2</td>\n",
       "      <td>716.1</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   日期  大气温度  气象站气压  相对湿度  类别\n",
       "0 2022-02-28 23:00:00  13.9  719.6    40   1\n",
       "1 2022-02-28 20:00:00  13.4  716.0    47   2\n",
       "2 2022-02-28 17:00:00  16.6  713.7    27   3\n",
       "3 2022-02-28 14:00:00  16.9  714.1    33   1\n",
       "4 2022-02-28 11:00:00  12.2  716.1    67   3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取excel文件\n",
    "df = pd.read_excel(\"pydata02_1678066030739.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2b9b92d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['大气温度'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980deda4",
   "metadata": {},
   "source": [
    "#### 102、在使用to_csv函数方法导出数据集pydata02时encoding参数的默认值为 (A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f65d9019",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看to_csv帮助文档, 默认为utf8,选A或C\n",
    "df.to_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5811ce50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 题外话，df有中文，用默认utf8是出现乱码的，需要设置为gbk\n",
    "df.to_csv(\"test.csv\")# 中文乱码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4e06a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"test.csv\", encoding='gbk') # 中文无乱码"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41166e82",
   "metadata": {},
   "source": [
    "#### 103、请问pydata02.xlsx中“大气温度’列的缺失值数量是多少 (D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7ba84b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 224 entries, 0 to 223\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   日期      224 non-null    datetime64[ns]\n",
      " 1   大气温度    224 non-null    float64       \n",
      " 2   气象站气压   224 non-null    float64       \n",
      " 3   相对湿度    224 non-null    int64         \n",
      " 4   类别      224 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(2)\n",
      "memory usage: 8.9 KB\n"
     ]
    }
   ],
   "source": [
    "# 方法1： 总共224行，大气温度Non-null有224行，缺失值为224-224=0行\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e7738ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法2： 用isnull()函数,缺失为True,非确实为False，True的值是1，False是0，求和即可\n",
    "df['大气温度'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43c891f9",
   "metadata": {},
   "source": [
    "#### 104、对pydata02小于等于1的大气温度进行求和，结果为 (D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b392e5b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19999999999999993"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition = (df['大气温度'] <= 1)\n",
    "df[condition]['大气温度'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d581420",
   "metadata": {},
   "source": [
    "#### 105、请使用count()函数，求出类别为3的组，大气温度的数量是(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65dfd1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition = (df['类别'] == 3)\n",
    "df[condition]['大气温度'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d4cb8cb",
   "metadata": {},
   "source": [
    "#### 106、对pydata02的大气温度进行ARMA模型拟合，下列为3阶AR和2阶MA模型的拟合效果图是(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b3e480",
   "metadata": {},
   "source": [
    "#### AR模型: 用历史数据的线性组合来拟合当前数据，p阶AR模型指的是用过去p个历史数据线性的线性组合拟合当前数据\n",
    "#### MA模型: 用历史数据的随机干扰来线性拟合当前数据，q阶MA模型指用获取q个历史数据的随机干扰来线性拟合当前数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ff00a0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\python\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "df = pd.read_excel(\"pydata02_1678066030739.xlsx\")\n",
    "# 转换日期格式\n",
    "df['日期'] = pd.to_datetime(df['日期'])\n",
    "df = df.sort_values('日期', ascending=True)\n",
    "# ARMA模型\n",
    "data = df['大气温度'].to_numpy()\n",
    "model_MA = ARIMA(data, order=(3, 0, 2))  # 3阶AR和2阶MA\n",
    "result_model_MA = model_MA.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "09ec4470",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文和数字负号的显示\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "84ec24c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f7aa445dc0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()  # 创建画布和坐标轴\n",
    "x = df['日期'].to_numpy()\n",
    "y1 = df['大气温度'].to_numpy()\n",
    "y2 = result_model_MA.fittedvalues\n",
    "ax.plot(x, y1, color='blue', linestyle='-', label='True')\n",
    "ax.plot(x, y2, color='red', linestyle='--', label='modle')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74c52abf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
